How DIDO Works

DIDO is intended as a generic approach to solving optimal control problems. This means that it should readily integrate into any autonomous system no matter what types of input that system is processing, so long as it uses the MATLAB computing platform running in Microsoft Windows. By autonomous system, we mean a combination of hardware and software that functions together as a robotic entity, and determines on its own the sequence of actions it needs to perform in order to complete a given task. In a sense, an autonomous system is a robot that can solve its own problems.

It's because DIDO is so generic that researchers in both academics and industry have found ways to apply the software to solve a wide range of optimal control problems. We see this prominently in robotics where researchers can integrate DIDO with the existing software in an autonomous system. DIDO creator Ross said in an interview that one of his former students in particular has applied the software to ground robots with some amazing results. That same Ph.D. graduate has researched navigating multiple robotic vehicles through rough waters without them colliding.

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Another field where DIDO's getting traction is aeronautics. Earlier, we gave an example of all the conditions that go into determining how to steer an aircraft. That example wasn't arbitrary: We chose it because DIDO has been able to do just that. Besides aiding in finding flight trajectories for gliders, DIDO is also helping in fuel optimization for aircraft. The American Institute of Aeronautics and Astronautics (AIAA) has published studies from researchers around the world that have used DIDO in their work.

One unique DIDO application has been applying its optimal control calculations to steering an undersea glider, an unmanned autonomous system in the form of a winged underwater vehicle. Researchers at Virginia Tech looked at ways to ensure such a glider could move like a porpoise through the water, which is both energy efficient and useful when taking oceanographic surveys. One of the team's challenges was to address a stalling effect when transitioning from downward to upward trajectories. The team reported that DIDO was the easiest solution method for them to set up and run for their research, though they also reported getting similar calculations with another tool [source: Kraus, Cliff, Woolsey and Luby].

Throughout this article, we've looked at what optimal control problems are, how DIDO is helping to solve them and the innovative ways researchers have applied DIDO in various commercial and academic fields. Optimize your DIDO experience by checking out even more information on the next page.